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Adding harmonized teacher-forcing

This commit is contained in:
Eren Golge 2018-03-19 09:27:19 -07:00
parent db1b710263
commit 55349f7085
2 changed files with 11 additions and 3 deletions
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10
layers/tacotron.py
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@ -286,7 +286,15 @@ class Decoder(nn.Module):
memory_input = initial_memory memory_input = initial_memory
while True: while True:
if t > 0: if t > 0:
memory_input = outputs[-1] if greedy else memory[t - 1] if greedy:
memory_input = outputs[-1]
else:
# combine prev. model output and prev. real target
memory_input = torch.div(outputs[-1] + memory[t-1], 2.0)
# add a random noise
noise = torch.autograd.Variable(
memory_input.data.new(memory_input.size()).normal_(0.0, 1.0))
memory_input = memory_input + noise
# Prenet # Prenet
processed_memory = self.prenet(memory_input) processed_memory = self.prenet(memory_input)

2
models/tacotron.py
View File

@ -34,7 +34,7 @@ class Tacotron(nn.Module):
# (B, T', mel_dim*r) # (B, T', mel_dim*r)
mel_outputs, alignments = self.decoder( mel_outputs, alignments = self.decoder(
encoder_outputs, mel_specs, input_lengths=input_lengths) encoder_outputs, mel_specs)
# Post net processing below # Post net processing below